Overview

Dataset statistics

Number of variables12
Number of observations569
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory53.5 KiB
Average record size in memory96.2 B

Variable types

Numeric11
Categorical1

Alerts

radius_mean is highly correlated with perimeter_mean and 3 other fieldsHigh correlation
perimeter_mean is highly correlated with radius_mean and 4 other fieldsHigh correlation
area_mean is highly correlated with radius_mean and 3 other fieldsHigh correlation
smoothness_mean is highly correlated with compactness_mean and 4 other fieldsHigh correlation
compactness_mean is highly correlated with perimeter_mean and 4 other fieldsHigh correlation
concavity_mean is highly correlated with radius_mean and 5 other fieldsHigh correlation
concave points_mean is highly correlated with radius_mean and 5 other fieldsHigh correlation
symmetry_mean is highly correlated with smoothness_mean and 1 other fieldsHigh correlation
fractal_dimension_mean is highly correlated with smoothness_meanHigh correlation
radius_mean is highly correlated with perimeter_mean and 4 other fieldsHigh correlation
perimeter_mean is highly correlated with radius_mean and 4 other fieldsHigh correlation
area_mean is highly correlated with radius_mean and 3 other fieldsHigh correlation
smoothness_mean is highly correlated with compactness_mean and 4 other fieldsHigh correlation
compactness_mean is highly correlated with radius_mean and 6 other fieldsHigh correlation
concavity_mean is highly correlated with radius_mean and 6 other fieldsHigh correlation
concave points_mean is highly correlated with radius_mean and 5 other fieldsHigh correlation
symmetry_mean is highly correlated with smoothness_mean and 2 other fieldsHigh correlation
fractal_dimension_mean is highly correlated with smoothness_mean and 1 other fieldsHigh correlation
radius_mean is highly correlated with perimeter_mean and 2 other fieldsHigh correlation
perimeter_mean is highly correlated with radius_mean and 2 other fieldsHigh correlation
area_mean is highly correlated with radius_mean and 2 other fieldsHigh correlation
compactness_mean is highly correlated with concavity_mean and 1 other fieldsHigh correlation
concavity_mean is highly correlated with compactness_mean and 1 other fieldsHigh correlation
concave points_mean is highly correlated with radius_mean and 4 other fieldsHigh correlation
diagnosis is highly correlated with radius_mean and 6 other fieldsHigh correlation
radius_mean is highly correlated with diagnosis and 5 other fieldsHigh correlation
texture_mean is highly correlated with diagnosisHigh correlation
perimeter_mean is highly correlated with diagnosis and 5 other fieldsHigh correlation
area_mean is highly correlated with diagnosis and 5 other fieldsHigh correlation
smoothness_mean is highly correlated with compactness_mean and 4 other fieldsHigh correlation
compactness_mean is highly correlated with diagnosis and 8 other fieldsHigh correlation
concavity_mean is highly correlated with diagnosis and 8 other fieldsHigh correlation
concave points_mean is highly correlated with diagnosis and 7 other fieldsHigh correlation
symmetry_mean is highly correlated with smoothness_mean and 4 other fieldsHigh correlation
fractal_dimension_mean is highly correlated with smoothness_mean and 3 other fieldsHigh correlation
id has unique values Unique
concavity_mean has 13 (2.3%) zeros Zeros
concave points_mean has 13 (2.3%) zeros Zeros

Reproduction

Analysis started2022-07-30 19:56:47.247749
Analysis finished2022-07-30 19:56:59.800604
Duration12.55 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

UNIQUE

Distinct569
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30371831.43
Minimum8670
Maximum911320502
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-07-30T15:56:59.900721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum8670
5-th percentile90267
Q1869218
median906024
Q38813129
95-th percentile90424461.4
Maximum911320502
Range911311832
Interquartile range (IQR)7943911

Descriptive statistics

Standard deviation125020585.6
Coefficient of variation (CV)4.116333448
Kurtosis42.19319416
Mean30371831.43
Median Absolute Deviation (MAD)44225
Skewness6.473751802
Sum1.728157208 × 1010
Variance1.563014683 × 1016
MonotonicityNot monotonic
2022-07-30T15:57:00.011117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8423021
 
0.2%
902501
 
0.2%
9013151
 
0.2%
90135791
 
0.2%
90135941
 
0.2%
90138381
 
0.2%
9015491
 
0.2%
9018361
 
0.2%
902511
 
0.2%
90130051
 
0.2%
Other values (559)559
98.2%
ValueCountFrequency (%)
86701
0.2%
89131
0.2%
89151
0.2%
90471
0.2%
857151
0.2%
862081
0.2%
862111
0.2%
863551
0.2%
864081
0.2%
864091
0.2%
ValueCountFrequency (%)
9113205021
0.2%
9113205011
0.2%
9112962021
0.2%
9112962011
0.2%
9111573021
0.2%
9010343021
0.2%
9010343011
0.2%
8810948021
0.2%
8810465021
0.2%
8710015021
0.2%

diagnosis
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
B
357 
M
212 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters569
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
B357
62.7%
M212
37.3%

Length

2022-07-30T15:57:00.113111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-30T15:57:00.197990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
b357
62.7%
m212
37.3%

Most occurring characters

ValueCountFrequency (%)
B357
62.7%
M212
37.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter569
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B357
62.7%
M212
37.3%

Most occurring scripts

ValueCountFrequency (%)
Latin569
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B357
62.7%
M212
37.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII569
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B357
62.7%
M212
37.3%

radius_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct456
Distinct (%)80.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.12729174
Minimum6.981
Maximum28.11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-07-30T15:57:00.536372image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6.981
5-th percentile9.5292
Q111.7
median13.37
Q315.78
95-th percentile20.576
Maximum28.11
Range21.129
Interquartile range (IQR)4.08

Descriptive statistics

Standard deviation3.524048826
Coefficient of variation (CV)0.2494497099
Kurtosis0.8455216229
Mean14.12729174
Median Absolute Deviation (MAD)1.9
Skewness0.9423795717
Sum8038.429
Variance12.41892013
MonotonicityNot monotonic
2022-07-30T15:57:00.647458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.344
 
0.7%
11.713
 
0.5%
12.463
 
0.5%
13.053
 
0.5%
10.263
 
0.5%
13.853
 
0.5%
12.773
 
0.5%
13.173
 
0.5%
133
 
0.5%
15.463
 
0.5%
Other values (446)538
94.6%
ValueCountFrequency (%)
6.9811
0.2%
7.6911
0.2%
7.7291
0.2%
7.761
0.2%
8.1961
0.2%
8.2191
0.2%
8.5711
0.2%
8.5971
0.2%
8.5981
0.2%
8.6181
0.2%
ValueCountFrequency (%)
28.111
0.2%
27.421
0.2%
27.221
0.2%
25.731
0.2%
25.221
0.2%
24.631
0.2%
24.251
0.2%
23.511
0.2%
23.291
0.2%
23.271
0.2%

texture_mean
Real number (ℝ≥0)

HIGH CORRELATION

Distinct479
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.28964851
Minimum9.71
Maximum39.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-07-30T15:57:00.762316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum9.71
5-th percentile13.088
Q116.17
median18.84
Q321.8
95-th percentile27.15
Maximum39.28
Range29.57
Interquartile range (IQR)5.63

Descriptive statistics

Standard deviation4.301035768
Coefficient of variation (CV)0.2229711841
Kurtosis0.7583189724
Mean19.28964851
Median Absolute Deviation (MAD)2.81
Skewness0.6504495421
Sum10975.81
Variance18.49890868
MonotonicityNot monotonic
2022-07-30T15:57:00.876766image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.523
 
0.5%
16.853
 
0.5%
16.843
 
0.5%
19.833
 
0.5%
14.933
 
0.5%
17.463
 
0.5%
18.93
 
0.5%
15.73
 
0.5%
18.223
 
0.5%
20.222
 
0.4%
Other values (469)540
94.9%
ValueCountFrequency (%)
9.711
0.2%
10.381
0.2%
10.721
0.2%
10.821
0.2%
10.891
0.2%
10.911
0.2%
10.941
0.2%
11.281
0.2%
11.791
0.2%
11.891
0.2%
ValueCountFrequency (%)
39.281
0.2%
33.811
0.2%
33.561
0.2%
32.471
0.2%
31.121
0.2%
30.721
0.2%
30.621
0.2%
29.971
0.2%
29.811
0.2%
29.431
0.2%

perimeter_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct522
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.96903339
Minimum43.79
Maximum188.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-07-30T15:57:00.994003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum43.79
5-th percentile60.496
Q175.17
median86.24
Q3104.1
95-th percentile135.82
Maximum188.5
Range144.71
Interquartile range (IQR)28.93

Descriptive statistics

Standard deviation24.29898104
Coefficient of variation (CV)0.2642082899
Kurtosis0.9722135477
Mean91.96903339
Median Absolute Deviation (MAD)12.71
Skewness0.9906504254
Sum52330.38
Variance590.4404795
MonotonicityNot monotonic
2022-07-30T15:57:01.105352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82.613
 
0.5%
87.763
 
0.5%
134.73
 
0.5%
93.972
 
0.4%
82.692
 
0.4%
120.22
 
0.4%
107.12
 
0.4%
79.192
 
0.4%
114.22
 
0.4%
58.792
 
0.4%
Other values (512)546
96.0%
ValueCountFrequency (%)
43.791
0.2%
47.921
0.2%
47.981
0.2%
48.341
0.2%
51.711
0.2%
53.271
0.2%
54.091
0.2%
54.341
0.2%
54.421
0.2%
54.531
0.2%
ValueCountFrequency (%)
188.51
0.2%
186.91
0.2%
182.11
0.2%
174.21
0.2%
171.51
0.2%
166.21
0.2%
165.51
0.2%
158.91
0.2%
155.11
0.2%
153.51
0.2%

area_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct539
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean654.8891037
Minimum143.5
Maximum2501
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-07-30T15:57:01.227808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum143.5
5-th percentile275.78
Q1420.3
median551.1
Q3782.7
95-th percentile1309.8
Maximum2501
Range2357.5
Interquartile range (IQR)362.4

Descriptive statistics

Standard deviation351.9141292
Coefficient of variation (CV)0.5373644594
Kurtosis3.652302762
Mean654.8891037
Median Absolute Deviation (MAD)153.3
Skewness1.645732176
Sum372631.9
Variance123843.5543
MonotonicityNot monotonic
2022-07-30T15:57:01.347313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
512.23
 
0.5%
10752
 
0.4%
582.72
 
0.4%
399.82
 
0.4%
641.22
 
0.4%
394.12
 
0.4%
372.72
 
0.4%
477.32
 
0.4%
758.62
 
0.4%
11382
 
0.4%
Other values (529)548
96.3%
ValueCountFrequency (%)
143.51
0.2%
170.41
0.2%
178.81
0.2%
1811
0.2%
201.91
0.2%
203.91
0.2%
221.21
0.2%
221.31
0.2%
221.81
0.2%
224.51
0.2%
ValueCountFrequency (%)
25011
0.2%
24991
0.2%
22501
0.2%
20101
0.2%
18781
0.2%
18411
0.2%
17611
0.2%
17471
0.2%
16861
0.2%
16851
0.2%

smoothness_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct474
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0963602812
Minimum0.05263
Maximum0.1634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-07-30T15:57:01.461145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.05263
5-th percentile0.075042
Q10.08637
median0.09587
Q30.1053
95-th percentile0.11878
Maximum0.1634
Range0.11077
Interquartile range (IQR)0.01893

Descriptive statistics

Standard deviation0.01406412814
Coefficient of variation (CV)0.1459535813
Kurtosis0.8559749304
Mean0.0963602812
Median Absolute Deviation (MAD)0.0095
Skewness0.4563237648
Sum54.829
Variance0.0001977997003
MonotonicityNot monotonic
2022-07-30T15:57:01.583784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.10075
 
0.9%
0.1154
 
0.7%
0.10544
 
0.7%
0.10754
 
0.7%
0.10633
 
0.5%
0.1173
 
0.5%
0.10493
 
0.5%
0.10443
 
0.5%
0.10663
 
0.5%
0.11583
 
0.5%
Other values (464)534
93.8%
ValueCountFrequency (%)
0.052631
0.2%
0.062511
0.2%
0.064291
0.2%
0.065761
0.2%
0.066131
0.2%
0.068281
0.2%
0.068831
0.2%
0.069351
0.2%
0.06951
0.2%
0.069551
0.2%
ValueCountFrequency (%)
0.16341
0.2%
0.14471
0.2%
0.14251
0.2%
0.13981
0.2%
0.13711
0.2%
0.13351
0.2%
0.13261
0.2%
0.13231
0.2%
0.12911
0.2%
0.12861
0.2%

compactness_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct537
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1043409842
Minimum0.01938
Maximum0.3454
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-07-30T15:57:01.721258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.01938
5-th percentile0.04066
Q10.06492
median0.09263
Q30.1304
95-th percentile0.2087
Maximum0.3454
Range0.32602
Interquartile range (IQR)0.06548

Descriptive statistics

Standard deviation0.05281275793
Coefficient of variation (CV)0.5061554512
Kurtosis1.650130467
Mean0.1043409842
Median Absolute Deviation (MAD)0.03263
Skewness1.190123031
Sum59.37002
Variance0.0027891874
MonotonicityNot monotonic
2022-07-30T15:57:01.844296image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.11473
 
0.5%
0.12063
 
0.5%
0.076982
 
0.4%
0.057432
 
0.4%
0.038342
 
0.4%
0.15162
 
0.4%
0.11172
 
0.4%
0.11112
 
0.4%
0.20872
 
0.4%
0.10472
 
0.4%
Other values (527)547
96.1%
ValueCountFrequency (%)
0.019381
0.2%
0.023441
0.2%
0.02651
0.2%
0.026751
0.2%
0.031161
0.2%
0.032121
0.2%
0.033931
0.2%
0.033981
0.2%
0.034541
0.2%
0.035151
0.2%
ValueCountFrequency (%)
0.34541
0.2%
0.31141
0.2%
0.28671
0.2%
0.28391
0.2%
0.28321
0.2%
0.27761
0.2%
0.2771
0.2%
0.27681
0.2%
0.26651
0.2%
0.25761
0.2%

concavity_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct537
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08879931582
Minimum0
Maximum0.4268
Zeros13
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-07-30T15:57:01.976079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0049826
Q10.02956
median0.06154
Q30.1307
95-th percentile0.24302
Maximum0.4268
Range0.4268
Interquartile range (IQR)0.10114

Descriptive statistics

Standard deviation0.07971980871
Coefficient of variation (CV)0.8977525105
Kurtosis1.998637529
Mean0.08879931582
Median Absolute Deviation (MAD)0.04046
Skewness1.401179739
Sum50.5268107
Variance0.0063552479
MonotonicityNot monotonic
2022-07-30T15:57:02.107425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013
 
2.3%
0.12043
 
0.5%
0.11152
 
0.4%
0.033442
 
0.4%
0.11032
 
0.4%
0.10852
 
0.4%
0.1012
 
0.4%
0.019722
 
0.4%
0.029952
 
0.4%
0.10072
 
0.4%
Other values (527)537
94.4%
ValueCountFrequency (%)
013
2.3%
0.0006921
 
0.2%
0.00097371
 
0.2%
0.0011941
 
0.2%
0.0014611
 
0.2%
0.0014871
 
0.2%
0.0015461
 
0.2%
0.0015951
 
0.2%
0.0015971
 
0.2%
0.001861
 
0.2%
ValueCountFrequency (%)
0.42681
0.2%
0.42641
0.2%
0.41081
0.2%
0.37541
0.2%
0.36351
0.2%
0.35231
0.2%
0.35141
0.2%
0.33681
0.2%
0.33391
0.2%
0.32011
0.2%

concave points_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct542
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04891914587
Minimum0
Maximum0.2012
Zeros13
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-07-30T15:57:02.225551image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0056208
Q10.02031
median0.0335
Q30.074
95-th percentile0.12574
Maximum0.2012
Range0.2012
Interquartile range (IQR)0.05369

Descriptive statistics

Standard deviation0.03880284486
Coefficient of variation (CV)0.7932036459
Kurtosis1.066555703
Mean0.04891914587
Median Absolute Deviation (MAD)0.02014
Skewness1.171180081
Sum27.834994
Variance0.001505660769
MonotonicityNot monotonic
2022-07-30T15:57:02.352307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013
 
2.3%
0.028643
 
0.5%
0.14712
 
0.4%
0.057782
 
0.4%
0.022722
 
0.4%
0.023692
 
0.4%
0.023772
 
0.4%
0.025942
 
0.4%
0.052522
 
0.4%
0.020312
 
0.4%
Other values (532)537
94.4%
ValueCountFrequency (%)
013
2.3%
0.0018521
 
0.2%
0.0024041
 
0.2%
0.0029241
 
0.2%
0.0029411
 
0.2%
0.0031251
 
0.2%
0.0032611
 
0.2%
0.0033331
 
0.2%
0.0034721
 
0.2%
0.0041671
 
0.2%
ValueCountFrequency (%)
0.20121
0.2%
0.19131
0.2%
0.18781
0.2%
0.18451
0.2%
0.18231
0.2%
0.16891
0.2%
0.1621
0.2%
0.16041
0.2%
0.15951
0.2%
0.15621
0.2%

symmetry_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct432
Distinct (%)75.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1811618629
Minimum0.106
Maximum0.304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-07-30T15:57:02.479360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.106
5-th percentile0.1415
Q10.1619
median0.1792
Q30.1957
95-th percentile0.23072
Maximum0.304
Range0.198
Interquartile range (IQR)0.0338

Descriptive statistics

Standard deviation0.02741428134
Coefficient of variation (CV)0.1513247926
Kurtosis1.287932992
Mean0.1811618629
Median Absolute Deviation (MAD)0.0171
Skewness0.7256089734
Sum103.0811
Variance0.0007515428212
MonotonicityNot monotonic
2022-07-30T15:57:02.609701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.17144
 
0.7%
0.17694
 
0.7%
0.18934
 
0.7%
0.16014
 
0.7%
0.17174
 
0.7%
0.18613
 
0.5%
0.19663
 
0.5%
0.19253
 
0.5%
0.15063
 
0.5%
0.17393
 
0.5%
Other values (422)534
93.8%
ValueCountFrequency (%)
0.1061
0.2%
0.11671
0.2%
0.12031
0.2%
0.12151
0.2%
0.1221
0.2%
0.12741
0.2%
0.13051
0.2%
0.13081
0.2%
0.13371
0.2%
0.13391
0.2%
ValueCountFrequency (%)
0.3041
0.2%
0.29061
0.2%
0.27431
0.2%
0.26781
0.2%
0.26551
0.2%
0.25971
0.2%
0.25951
0.2%
0.25691
0.2%
0.25561
0.2%
0.25481
0.2%

fractal_dimension_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct499
Distinct (%)87.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06279760984
Minimum0.04996
Maximum0.09744
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2022-07-30T15:57:02.775389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.04996
5-th percentile0.053926
Q10.0577
median0.06154
Q30.06612
95-th percentile0.07609
Maximum0.09744
Range0.04748
Interquartile range (IQR)0.00842

Descriptive statistics

Standard deviation0.007060362795
Coefficient of variation (CV)0.1124304382
Kurtosis3.00589212
Mean0.06279760984
Median Absolute Deviation (MAD)0.00422
Skewness1.304488813
Sum35.73184
Variance4.98487228 × 10-5
MonotonicityNot monotonic
2022-07-30T15:57:02.889458image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.061133
 
0.5%
0.059133
 
0.5%
0.059073
 
0.5%
0.056673
 
0.5%
0.067823
 
0.5%
0.058662
 
0.4%
0.06022
 
0.4%
0.056742
 
0.4%
0.064122
 
0.4%
0.060192
 
0.4%
Other values (489)544
95.6%
ValueCountFrequency (%)
0.049961
0.2%
0.050241
0.2%
0.050251
0.2%
0.050441
0.2%
0.050541
0.2%
0.050961
0.2%
0.051761
0.2%
0.051771
0.2%
0.051851
0.2%
0.052231
0.2%
ValueCountFrequency (%)
0.097441
0.2%
0.095751
0.2%
0.095021
0.2%
0.092961
0.2%
0.08981
0.2%
0.087431
0.2%
0.08451
0.2%
0.082611
0.2%
0.082431
0.2%
0.081421
0.2%

Interactions

2022-07-30T15:56:58.390175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:47.560208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:48.550725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:49.514833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:50.561119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:51.627795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:52.640859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:53.961126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:55.009261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:56.006419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:57.134040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:58.480535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:47.644415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:48.634382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:49.603140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:50.653189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:51.717143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:52.728178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:54.051548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:55.095274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:56.099100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:57.236277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:58.578096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:47.725822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:48.716811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:49.691896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:50.746401image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:51.800822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:52.815177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:54.137220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:55.175648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:56.193311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:57.329832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:58.680375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:47.814093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:48.803929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:49.790633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:50.844848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:51.892043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:52.906943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:54.231433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:55.265374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:56.296173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:57.432080image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:58.785731image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:47.905414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:48.895179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:49.889275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:50.943249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:51.983706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:53.003346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:54.333178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:55.363890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:56.398625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:57.538120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:58.876523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:47.987403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:48.976265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:49.977960image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:51.032230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:52.071126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:53.385088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:54.423121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:55.449280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:56.497874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:57.634729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:58.969341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:48.073434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:49.064355image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:50.069374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:51.127854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:52.162540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:53.476267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:54.517437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:55.540557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:56.603285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:57.739229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:59.066140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:48.165490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:49.152975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:50.167832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:51.229044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:52.260175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:53.573938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:54.614541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:55.631383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:56.708556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:57.842069image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:59.157994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:48.262257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:49.236063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:50.258242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:51.324357image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:52.351218image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:53.665191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:54.706032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:55.720446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:56.808373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:57.937403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:59.263010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:48.362482image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:49.331216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:50.366315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:51.429474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:52.451058image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:53.764142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:54.812938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:55.816761image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:56.921164image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:58.045606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:59.372298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:48.458643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:49.428366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:50.469161image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:51.533382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:52.551292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:53.869884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:54.918921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:55.915479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:57.034971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-07-30T15:56:58.156552image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-07-30T15:57:02.990188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-30T15:57:03.163614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-30T15:57:03.349240image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-30T15:57:03.508323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-30T15:56:59.527470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-30T15:56:59.726027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

iddiagnosisradius_meantexture_meanperimeter_meanarea_meansmoothness_meancompactness_meanconcavity_meanconcave points_meansymmetry_meanfractal_dimension_mean
0842302M17.9910.38122.801001.00.118400.277600.300100.147100.24190.07871
1842517M20.5717.77132.901326.00.084740.078640.086900.070170.18120.05667
284300903M19.6921.25130.001203.00.109600.159900.197400.127900.20690.05999
384348301M11.4220.3877.58386.10.142500.283900.241400.105200.25970.09744
484358402M20.2914.34135.101297.00.100300.132800.198000.104300.18090.05883
5843786M12.4515.7082.57477.10.127800.170000.157800.080890.20870.07613
6844359M18.2519.98119.601040.00.094630.109000.112700.074000.17940.05742
784458202M13.7120.8390.20577.90.118900.164500.093660.059850.21960.07451
8844981M13.0021.8287.50519.80.127300.193200.185900.093530.23500.07389
984501001M12.4624.0483.97475.90.118600.239600.227300.085430.20300.08243

Last rows

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